1. Technical Field
The present invention relates to an optimal maximum likelihood signal detection method and apparatus for a MIMO system that can provide optimal signal detection of the same quality as maximum likelihood detection by setting an optimal threshold for selecting the number of surviving candidate symbols variably for the adaptive detection of signals in each detection layer according to the channel environment or the signal-to-noise ratio (SNR) based on the QR decomposition (QRD)-M algorithm in a MIMO system.
2. Description of the Related Art
A multiple-input multiple-output (MIMO) system can increase system efficiency without additional bandwidth or increased transmission power. For this reason, the MIMO system is widely being employed or considered in current communication standards.
Maximum likelihood (ML) detection is known as an optimal algorithm for signal detection in a MIMO system. Although ML detection provides an optimal error performance, there is the drawback that its computational complexity grows exponentially according to increases in the number of multiple inputs at the transmitter and in modulation order. Due to such increase in computational complexity, it may actually be impossible to implement in communication and/or broadcasting systems having a large number of multiple inputs (the number of antennas or the number of channels) or a high modulation order. For example, the Long Term Evolution (LTE)-advanced standard employs the Single Carrier Frequency Division Multiple Access (SC-FDMA) technology for the uplink, and if 4-Quadrature Amplitude Modulation (QAM) and 16-QAM modulation were applied and two resource blocks (RB: 1 RB being 12 subchannels) were allotted to a user, then a ML detection for a receiver would require computing the Euclidean norm 424=248 times (approximately 281 trillion times) and 1624=296 times (approximately 7.9×1028 times), respectively, which cannot be achieved with a typical personal computer.
In recent times, near ML detection methods have been proposed, such as the QRD-M detection method, which when applied in a signal detection algorithm provides a bit error rate (BER) performance comparable to that of the ML detection method. This can reduce complexity compared to ML detection while providing a performance comparable to the BER of ML detection, but cannot provide the same BER performance as does ML detection, and hence is referred to as “near ML” detection.
For instance, the QRD-M detection algorithm performs signal detection by selecting M candidate symbols in each detection layer and can reduce complexity compared to ML detection. However, the conventional QRD-M detection algorithm may constantly select candidate symbols for each detection layer. As a result, in the conventional QRD-M detection algorithm, the number of candidate symbols M should be set to a large value in order to obtain a comparable BER performance to the ML detection, and since a fixed M number of candidate symbols are selected for each detection layer even when the received SNR is high, the efficiency of the computational complexity may be degraded.
An adaptive threshold method was proposed as an improvement in which M candidate symbols are selected variably in each detection layer, but because an optimal threshold cannot be applied for each detection layer, it cannot achieve the same BER performance as that of ML detection and remains at a performance level comparable to the near ML detection methods.